Field of the Invention
The invention concerns a method and a device for segmenting a medical examination object by the application of quantitative magnetic resonance (MR) imaging methods for medical issues and segmenting processes.
Description of the Prior Art
The significance of segmenting processes, i.e. the depiction of specific sections of examination objects, has increased greatly in the past years in MR imaging in general and in orthopedic MR imaging in particular. One application example is the segmenting of cartilage tissue and the subsequent automatic evaluation of thickness, volume, etc. to detect potential cartilage damage as early as possible and to objectively quantify a potential successful treatment or course over time of diseases (e.g. arthroses). Other areas are e.g. segmenting of tumors or vessel deposits. Segmenting methods are also used to depict bones or fluids or in MR PET attenuation correction. Current segmenting algorithms for structures having complex geometries, such as e.g. joints, are often computing-intensive and in particular in conventional MR images, which generate only one qualitative contrast, prone to error.
Current segmenting methods are based on one or more MR image data sets with a specific contrast weighting. The contrast of the image is generally chosen such that there is an optimally high contrast between the types of tissue to be segmented.
Segmenting algorithms must generally first of all learn the used contrast, for example by adjusting the choice of parameters or extraction of model parameters from training data, since, depending on the choice of protocol parameters, a different contrast may exist. Errors can occur during segmenting if the scan protocol is changed too much. In addition there is the compounding factor that the qualitative contrast of conventional MR images can vary from one MR system to another, from one used coil to another, and even from one day to another. Further image differences can occur due to the patient positioning and choice of coil, scan adjustments, noise level, manufacturer, SW versions, etc. To achieve more robustness modern segmenting algorithms often use prior knowledge about the shape of the structures to be segmented (atlases, shape models). The results of methods that use such qualitative data can turn out badly, however, as soon as a case deviates too much from the models obtained from training data (e.g. rare pathologies). In general a reproducible scan result and therewith reliable segmenting cannot always be guaranteed.